CN112651421B - Infrared thermal imaging power transmission line anti-external-damage monitoring system and modeling method thereof - Google Patents
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Abstract
The invention provides an infrared thermal imaging power transmission line anti-external damage monitoring system and a modeling method thereof, comprising the following steps: the convolution neural network module is used for extracting the characteristics of the infrared transmission line external broken and segmented image; the two-way circulation neural network module is provided with five modules and is positioned behind the convolution neural network module; the decoder module is used for gradually recovering the resolution of the feature map output by the bidirectional cyclic neural network module to the resolution of the input image; and the model training module is used for training the anti-external damage monitoring system. According to the infrared thermal imaging power transmission line external damage prevention monitoring system and the modeling method thereof, an external damage hidden danger segmentation network model based on the deep convolutional neural network and the bidirectional cyclic neural network is constructed, intelligent analysis can be carried out on an infrared image acquired by front-end inspection, the external damage hidden danger of the power transmission line is positioned, the monitoring effect is ensured, and the labor cost of personnel inspection is greatly reduced.
Description
Technical Field
The invention relates to the technical field of transmission line detection, in particular to an infrared thermal imaging transmission line external damage prevention monitoring system and a modeling method thereof.
Background
With the increase of national economy and living standard of China, the demand of electric power is increased increasingly, the power grid scale of an electric power system is enlarged, and the electric load is increased, so that the possibility of accidents such as equipment burning and the like caused by damage, faults and serious electric power equipment is increased. In order to avoid various electric power accidents as much as possible, it is imperative to reduce the major economic loss caused by the accidents, and the economic loss is imperative.
The inspection is time-consuming and labor-consuming in a manual mode, and the reliability is low. Real-time monitoring pictures or videos are transmitted to the background through a communication technology and a sensing technology, so that the inspection workload can be reduced, but background staff is still required to judge whether external hidden danger exists or not by naked eyes, the workload is large, omission is easy, and the monitoring intellectualization is not realized.
Disclosure of Invention
In order to solve the problems, the invention provides an infrared thermal imaging power transmission line external damage prevention monitoring system and a modeling method thereof, which construct an external damage hidden danger segmentation network model based on a deep convolutional neural network and a bidirectional cyclic neural network, can intelligently analyze an infrared image acquired by front-end inspection, locate the external damage hidden danger of the power transmission line, ensure the monitoring effect and greatly reduce the labor cost of personnel inspection.
In order to achieve the above purpose, the invention adopts a technical scheme that:
An infrared thermal imaging transmission line anti-outward-breakage monitoring system, comprising: the convolution neural network module is used for extracting the characteristics of the infrared transmission line external broken and segmented image and comprises two DWBlock modules and two residual modules, and the infrared image is sequentially output to the DWBlock module, the residual modules, the DWBlock module and the residual modules;
The two-way circulation neural network module is provided with five modules and is positioned behind the convolution neural network module; the decoder module is used for gradually recovering the resolution of the feature map output by the bidirectional cyclic neural network module to the resolution of the input image; and the decoder module outputs the model training module to the model training module, and the model training module is used for training the anti-external-damage monitoring system.
Further, the DWBlock module is sequentially composed of data filling, longitudinal convolution operation, batch normalization processing, convolution operation and batch normalization processing, wherein the number of input channels of the longitudinal convolution operation is the same as the number of convolution kernels.
Further, the data filling operation fuses the characteristic values; the method is characterized in that boundaries of the infrared image or the visible light image are expanded, and the batch normalization process for each input x i is as follows: x i＝(xi-u)/(sqrt((xi-v)2) +e), where u is the mean of the inputs { x 1,x2,x3,…,xn }, v is the variance of the inputs { x 1,x2,x3,…,xn }, e is a small bias preventing the denominator from going to 0.
Further, scale plus shift operations, i.e., x i＝scale*xi +shift, were performed on the batch normalized result x i, where scale and shift were learned.
Further, when the input is x, the residual module output is F (x) +x.
The invention also provides a modeling method of the infrared thermal imaging power transmission line anti-external damage monitoring system, which comprises the following steps: s10, acquiring infrared images through an infrared camera, and marking an infrared transmission line external broken and divided sample set; s20, constructing a convolutional neural network module, and inputting the infrared transmission line external broken and split sample set into the convolutional neural network module to obtain an infrared characteristic value; s30, constructing a bidirectional circulating neural network module, and inputting the infrared characteristic value into the bidirectional circulating neural network module for scanning according to columns and rows, wherein the specific formula is as follows:
wherein f represents a recurrent neural network RNN, the infrared eigenvalues are partitioned into i×j blocks, o is the result, z is the previous state, and p is the eigenvalues within the eigenvector blocks; s40, constructing a decoding module, inputting the fusion characteristic value into the decoding module, and outputting the decoding module to a softmax layer to complete modeling.
Compared with the prior art, the technical scheme of the invention has the following advantages:
According to the infrared thermal imaging power transmission line external damage prevention monitoring system and the modeling method thereof, an external damage hidden danger segmentation network model based on the deep convolutional neural network and the bidirectional cyclic neural network is constructed, intelligent analysis can be carried out on an infrared image acquired by front-end inspection, the external damage hidden danger of the power transmission line is positioned, the monitoring effect is ensured, and the labor cost of personnel inspection is greatly reduced.
Drawings
The technical solution of the present invention and its advantageous effects will be made apparent by the following detailed description of the specific embodiments of the present invention with reference to the accompanying drawings.
FIG. 1 is a block diagram of a deep convolutional neural network module according to one embodiment of the present invention;
FIG. 2 is a block diagram of a residual block according to an embodiment of the present invention;
fig. 3 is a flowchart of a modeling method of an infrared thermal imaging transmission line anti-external damage monitoring system according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
The embodiment provides an infrared thermal imaging transmission line anti-external damage monitoring system, which comprises a convolutional neural network module, a bidirectional cyclic neural network module, a decoder module and a model training module which are sequentially connected.
As shown in fig. 1, the convolutional neural network module includes two DWBlock modules and two residual modules, the infrared images are sequentially output to the DWBlock module, the residual modules, the DWBlock module and the residual modules, and the convolutional neural network module is used for extracting the infrared transmission line external broken and segmented image features.
The DWBlock module is sequentially composed of data filling, longitudinal convolution operation, batch normalization processing, convolution operation and batch normalization processing, wherein the number of input channels of the longitudinal convolution operation is the same as the number of convolution kernels. The data filling operation fuses the characteristic values; the method is characterized in that boundaries of the infrared image or the visible light image are expanded, and the batch normalization process for each input x i is as follows: x i＝(xi-u)/(sqrt((xi-v)2) +e), where u is the mean of the inputs { x 1,x2,x3,…,xn }, v is the variance of the inputs { x 1,x2,x3,…,xn }, e is a small bias preventing the denominator from going to 0. The batch normalized result x i was subjected to scale plus shift operation, i.e., x i＝scale＊xi +shift, where scale and shift were learned.
When the input is x, F (x) is a hidden layer operation, then the output of the general neural network is H (x) =f (x), and the output of the residual network is H (x) =f (x) +x, and the specific structure is as shown in fig. 2, and the residual block includes two parts: shortcut connection and residual part. F (x) is the residual, represented on the left side of the upper graph, wherein weightlayer represents the convolution operation, weightlayer is the 3*3 convolution layer, and the convolved feature map is added to x to obtain a new feature map.
The two-way cyclic neural network module is created after five two-way cyclic neural network modules are connected to the convolutional neural network module. The basic idea of the bi-directional recurrent neural network (BRNN) is to propose that each training sequence is two Recurrent Neural Networks (RNNs) forward and backward, respectively, and that both are connected to one output layer. This structure provides the output layer with complete past and future context information for each point in the input sequence.
The decoder module is an up-sampling layer formed by deconvolution of a plurality of layers and is used for gradually recovering the resolution of the feature map output by the bidirectional cyclic neural network module to the resolution of the input image.
The decoder module outputs to the model training module, whose main design objective is to obtain dense predictions that are the same as the original input resolution. By means of the decoder module, the resolution of the feature map gradually reverts to the resolution of the input image.
The decoder module outputs the model training module, and the model training module is used for training the anti-external-damage monitoring system.
The invention also provides a modeling method of the infrared thermal imaging power transmission line anti-external damage monitoring system, as shown in fig. 3, comprising the following steps: s10, acquiring infrared images through an infrared camera, and marking an infrared transmission line external broken and divided sample set. S20, constructing a convolutional neural network module, and inputting the infrared transmission line external broken and split sample set to the convolutional neural network module to obtain an infrared characteristic value. S30, constructing a bidirectional circulating neural network module, and inputting the infrared characteristic value into the bidirectional circulating neural network module for scanning according to columns and rows, wherein the specific formula is as follows:
Where f represents the recurrent neural network RNN, the infrared eigenvalues are partitioned into i x j blocks, o is the result, z is the previous state, and p is the eigenvalue within the eigenvector. And S40, constructing a decoding module, inputting the fusion characteristic value into the decoding module, and outputting the decoding module to a softmax layer to complete modeling. Where y i represents the softmax ith output value, i represents the category index, ctotal number of categories, v i represents the decoding module's ith output.
The foregoing description is only exemplary embodiments of the present invention and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present invention or directly or indirectly applied to other related technical fields are included in the scope of the present invention.
Claims (5)
1. An infrared thermal imaging transmission line prevents broken monitoring system outward, which characterized in that includes: the convolution neural network module is used for extracting the characteristics of the infrared transmission line external broken and segmented image and comprises two DWBlock modules and two residual modules, and the infrared image is sequentially output to the DWBlock module, the residual modules, the DWBlock module and the residual modules; the two-way circulation neural network module is provided with five modules and is positioned behind the convolution neural network module; the decoder module is used for gradually recovering the resolution of the feature map output by the bidirectional cyclic neural network module to the resolution of the input image; the decoder module outputs the data to the model training module, and the model training module is used for training the anti-external damage monitoring system;
The DWBlock module is sequentially composed of data filling, longitudinal convolution operation, batch normalization processing, convolution operation and batch normalization processing, wherein the number of input channels of the longitudinal convolution operation is the same as the number of convolution kernels.
2. The infrared thermal imaging transmission line anti-outward-breakage monitoring system according to claim 1, wherein the data filling operation refers to expanding boundaries of infrared images or visible light images, and the batch normalization process for each input x i is as follows: x' i＝(xi-u)/(sqrt((xi-v)2) +e), where u is the mean of the inputs { x 1,x2,x3,…,xn }, v is the variance of the inputs { x 1,x2,x3,…,xn }, e is a small bias preventing the denominator from going to 0.
3. The infrared thermal imaging transmission line anti-external damage monitoring system according to claim 2, wherein the batch normalization result x' i is subjected to scale plus shift operation, namely x "i＝scale＊x'i +shift, wherein scale and shift are obtained through learning.
4. The infrared thermal imaging transmission line anti-external damage monitoring system of claim 3, wherein when the input is x, the residual module output is F (x) +x.
5. The modeling method of an infrared thermal imaging transmission line anti-external damage monitoring system according to claim 4, comprising the steps of: s10, acquiring infrared images through an infrared camera, and marking an infrared transmission line external broken and divided sample set; s20, constructing a convolutional neural network module, and inputting the infrared transmission line external broken and split sample set into the convolutional neural network module to obtain an infrared characteristic value; s30, constructing a bidirectional circulating neural network module, and inputting the infrared characteristic value into the bidirectional circulating neural network module for scanning according to columns and rows, wherein the specific formula is as follows:
wherein f represents a recurrent neural network RNN, the infrared eigenvalues are partitioned into i×j blocks, o is the result, z is the previous state, and p is the eigenvalues within the eigenvector blocks; and S40, constructing a decoding module, inputting the fusion characteristic value into the decoding module, and outputting the decoding module to a softmax layer to complete modeling.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109993055A (en) * | 2019-02-21 | 2019-07-09 | 北京以萨技术股份有限公司 | A kind of continuous abnormal image detecting method neural network based |
| CN110335260A (en) * | 2019-06-27 | 2019-10-15 | 华东送变电工程有限公司 | Power cable damage detection method based on light convolution neural network |
| CN110418210A (en) * | 2019-07-12 | 2019-11-05 | 东南大学 | A kind of video presentation generation method exported based on bidirectional circulating neural network and depth |
| CN110852199A (en) * | 2019-10-28 | 2020-02-28 | 中国石化销售股份有限公司华南分公司 | Foreground extraction method based on double-frame coding and decoding model |
| CN111062395A (en) * | 2019-11-27 | 2020-04-24 | 北京理工大学 | Real-time video semantic segmentation method |
| CN111091130A (en) * | 2019-12-13 | 2020-05-01 | 南京邮电大学 | Real-time image semantic segmentation method and system based on lightweight convolutional neural network |
| AU2020101229A4 (en) * | 2020-07-02 | 2020-08-06 | South China University Of Technology | A Text Line Recognition Method in Chinese Scenes Based on Residual Convolutional and Recurrent Neural Networks |
-
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- 2020-09-04 CN CN202010919200.2A patent/CN112651421B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109993055A (en) * | 2019-02-21 | 2019-07-09 | 北京以萨技术股份有限公司 | A kind of continuous abnormal image detecting method neural network based |
| CN110335260A (en) * | 2019-06-27 | 2019-10-15 | 华东送变电工程有限公司 | Power cable damage detection method based on light convolution neural network |
| CN110418210A (en) * | 2019-07-12 | 2019-11-05 | 东南大学 | A kind of video presentation generation method exported based on bidirectional circulating neural network and depth |
| CN110852199A (en) * | 2019-10-28 | 2020-02-28 | 中国石化销售股份有限公司华南分公司 | Foreground extraction method based on double-frame coding and decoding model |
| CN111062395A (en) * | 2019-11-27 | 2020-04-24 | 北京理工大学 | Real-time video semantic segmentation method |
| CN111091130A (en) * | 2019-12-13 | 2020-05-01 | 南京邮电大学 | Real-time image semantic segmentation method and system based on lightweight convolutional neural network |
| AU2020101229A4 (en) * | 2020-07-02 | 2020-08-06 | South China University Of Technology | A Text Line Recognition Method in Chinese Scenes Based on Residual Convolutional and Recurrent Neural Networks |
Non-Patent Citations (1)
| Title |
|---|
| 基于双向循环神经网络的跌倒行为识别;佃松宜等;《计算机工程与设计》;第41卷(第07期);1-6 * |
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